overlap

library(immunarch)
library(data.table)
library(gridExtra)
library(plotly)

Ovarian Cancer TRB

#### BLOOD
immdata_ova_trb<-repLoad(OC_path)

Healthy TRB

#### BLOOD
immdata_H_trb<-repLoad(H_path)

Combine OC and H

data <- c(immdata_ova_trb$data, immdata_H_trb$data)

Before subsampling

copy <-data

public_b <- repOverlap(copy, .method = "public", .verbose = F, .col = "aa")
public_b_df = as.data.frame(public_b)

f <- plot_ly(x = colnames(public_b_df), y = rownames(public_b_df), z = as.matrix(public_b_df), 
             type = "heatmap", 
             zauto = F, zmin = 0, zmax = 4000)

f <- f %>% layout(title = 'Repertoire Overlap TRB Blood OC and H',
         xaxis = list(title = 'Sample', tickangle=-90),
         yaxis = list(title = 'Sample'))
f

Low TRB clones

clones_trb<-repExplore(data, .method = "clones")
trb_blood_order = arrange(clones_trb, Clones)
rownames(trb_blood_order) = c()
head(trb_blood_order)

##     Sample Clones
## 1 21_TRB_H   1725
## 2 49_TRB_H   7328
## 3 48_TRB_H  17252
## 4 OVA9_TRB  19477
## 5  1_TRB_H  20394
## 6 37_TRB_H  21989

Subsample Omit 21_H, 49_H

data[which(names(data) %in% c("21_TRB_H", "49_TRB_H"))] <- NULL
sub_b = repSample(data, .method = "downsample", .n = 17252)

after subsampling

copy_data_trb <-sub_b

public_b <- repOverlap(copy_data_trb, .method = "public", .verbose = F, .col = "aa") #%>% vis()
public_b_df = as.data.frame(public_b)

f_a <- plot_ly(x = colnames(public_b_df), y = rownames(public_b_df), z = as.matrix(public_b_df), 
               type = "heatmap", 
               zauto = F, zmin = 0, zmax = 200)

f_a <- f_a %>% layout(title = 'Repertoire Overlap TRB Blood OC and H',
         xaxis = list(title = 'Sample', tickangle=-90),
         yaxis = list(title = 'Sample'))
f_a